Clustering and classification are important operations in certain data mining applications. For instance, data within a dataset may need to be clustered and/or classified in a data-driven decision support system that is used to assist a user in searching and automatically organizing content, such as recorded television programs, electronic program guide entries, and other types of multimedia content.
Generally, many clustering and classification algorithms work well when the dataset is numerical (i.e., when data within the dataset are all related by some inherent similarity metric or natural order). Category datasets describe multiple attributes or categories that are often discrete, and therefore, lack a natural distance or proximity measure between them.
Accordingly, it would be beneficial to provide a system and method capable of clustering and classifying a category dataset.